Resource allocation for edge computing with multiple tenant configurations - Archive ouverte HAL Accéder directement au contenu
Communication Dans Un Congrès Année : 2020

Resource allocation for edge computing with multiple tenant configurations

Résumé

Edge Computing (EC) consists in deploying computational resources, e.g., memory, CPUs, at the Edge of the network, e.g., base stations, access points, and run there a part of the computation currently running on the Cloud. This approach promises to reduce latency, inter-domain traffic and enhance user experience. Since resources at the Edge are scarce, resource allocation is crucial for EC. While most of the studies assume users interact directly with the Edge submitting a sequence of tasks, we instead consider that users will interact with different Service Providers (SPs), as they currently do in the Web. We therefore consider the case of a Network Operator (NO) that owns the resources at the Edge and must decide how much resource to allocate to the different tenants (SPs). We propose MORA, a polynomial time strategy which allows the NO to maximize its utility, which can be inter-domain traffic savings, improved users' QoE or other metrics of interest. The core of MORA is that (i) it exploits service elasticity, i.e., the fact that services can adapt to the resources allocated by the NO and rely on a remote Cloud for the excess of computation, (ii) it is suitable for micro-services architecture, which decomposes a single service in a set of components, which MORA places in the different computational nodes of the Edge and (iii) it copes with multi-dimensional resources, e.g., memory and CPUs. After analyzing the properties of the algorithm, we show numerically that it performs close to the optimum. To guarantee reproducibility, the numerical evaluation is performed on publicly available traces from Google and Alibaba clusters and in synthetic scenarios and our code is open source.
Fichier principal
Vignette du fichier
_SAC2020__Resource_Allocation_for_Edge_Computing_with_Multiple_Tenant_Configurations.pdf (1.09 Mo) Télécharger le fichier
Origine : Fichiers produits par l'(les) auteur(s)
Loading...

Dates et versions

hal-02391242 , version 1 (03-12-2019)
hal-02391242 , version 2 (10-12-2019)

Identifiants

Citer

Andrea Araldo, Alessandro Di Stefano, Antonella Di Stefano. Resource allocation for edge computing with multiple tenant configurations. SAC 2020: 35th Symposium On Applied Computing, Mar 2020, Brno, Czech Republic. pp.1190-1199, ⟨10.1145/3341105.3374026⟩. ⟨hal-02391242v2⟩
132 Consultations
403 Téléchargements

Altmetric

Partager

Gmail Facebook X LinkedIn More